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1.  A role for central spindle proteins in cilia structure and function 
Cytoskeleton (Hoboken, N.J.)  2011;68(2):112-124.
Cytokinesis and ciliogenesis are fundamental cellular processes that require strict coordination of microtubule organization and directed membrane trafficking. These processes have been intensely studied, but there has been little indication that regulatory machinery might be extensively shared between them. Here, we show that several central spindle/midbody proteins (PRC1, MKLP-1, INCENP, centriolin) also localize in specific patterns at the basal body complex in vertebrate ciliated epithelial cells. Moreover, bioinformatic comparisons of midbody and cilia proteomes reveal a highly significant degree of overlap. Finally, we used temperature-sensitive alleles of PRC1/spd-1 and MKLP-1/zen-4 in C. elegans to assess ciliary functions while bypassing these proteins' early role in cell division. These mutants displayed defects in both cilia function and cilia morphology. Together, these data suggest the conserved re-use of a surprisingly large number of proteins in the cytokinetic apparatus and in cilia.
doi:10.1002/cm.20498
PMCID: PMC4089984  PMID: 21246755
Ciliogenesis; cytokinesis; PRC1; INCENP; MKLP-1; bioinformatics; cilia midbody
2.  Systematic prediction of gene function in Arabidopsis thaliana using a probabilistic functional gene network 
Nature protocols  2011;6(9):10.1038/nprot.2011.372.
AraNet is a functional gene network for the reference plant Arabidopsis and has been constructed in order to identify new genes associated with plant traits. It is highly predictive for diverse biological pathways and can be used to prioritize genes for functional screens. Moreover, AraNet provides a web-based tool with which plant biologists can efficiently discover novel functions of Arabidopsis genes (http://www.functionalnet.org/aranet/). This protocol explains how to conduct network-based prediction of gene functions using AraNet and how to interpret the prediction results. Functional discovery in plant biology is facilitated by combining candidate prioritization by AraNet with focused experimental tests.
doi:10.1038/nprot.2011.372
PMCID: PMC3654671  PMID: 21886106
3.  High-Throughput Immunofluorescence Microscopy Using Yeast Spheroplast Cell-Based Microarrays 
We have described a protocol for performing high-throughput immunofluorescence microscopy on microarrays of yeast cells. This approach employs immunostaining of spheroplasted yeast cells printed as high-density cell microarrays, followed by imaging using automated microscopy. A yeast spheroplast microarray can contain more than 5,000 printed spots, each containing cells from a given yeast strain, and is thus suitable for genome-wide screens focusing on single cell phenotypes, such as systematic localization or co-localization studies or genetic assays for genes affecting probed targets. We demonstrate the use of yeast spheroplast microarrays to probe microtubule and spindle defects across a collection of yeast strains harboring tetracycline-down-regulatable alleles of essential genes.
doi:10.1007/978-1-61737-970-3_7
PMCID: PMC3654672  PMID: 21104056
Yeast; immunofluorescence; high-throughput microscopy; cell microarrays; microtubule
4.  Revisiting the negative example sampling problem for predicting protein–protein interactions 
Bioinformatics  2011;27(21):3024-3028.
Motivation: A number of computational methods have been proposed that predict protein–protein interactions (PPIs) based on protein sequence features. Since the number of potential non-interacting protein pairs (negative PPIs) is very high both in absolute terms and in comparison to that of interacting protein pairs (positive PPIs), computational prediction methods rely upon subsets of negative PPIs for training and validation. Hence, the need arises for subset sampling for negative PPIs.
Results: We clarify that there are two fundamentally different types of subset sampling for negative PPIs. One is subset sampling for cross-validated testing, where one desires unbiased subsets so that predictive performance estimated with them can be safely assumed to generalize to the population level. The other is subset sampling for training, where one desires the subsets that best train predictive algorithms, even if these subsets are biased. We show that confusion between these two fundamentally different types of subset sampling led one study recently published in Bioinformatics to the erroneous conclusion that predictive algorithms based on protein sequence features are hardly better than random in predicting PPIs. Rather, both protein sequence features and the ‘hubbiness’ of interacting proteins contribute to effective prediction of PPIs. We provide guidance for appropriate use of random versus balanced sampling.
Availability: The datasets used for this study are available at http://www.marcottelab.org/PPINegativeDataSampling.
Contact: yungki@mail.utexas.edu; marcotte@icmb.utexas.edu
Supplementary Information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btr514
PMCID: PMC3198576  PMID: 21908540
5.  MSblender: a probabilistic approach for integrating peptide identifications from multiple database search engines 
Journal of proteome research  2011;10(7):2949-2958.
Shotgun proteomics using mass spectrometry is a powerful method for protein identification but suffers limited sensitivity in complex samples. Integrating peptide identifications from multiple database search engines is a promising strategy to increase the number of peptide identifications and reduce the volume of unassigned tandem mass spectra. Existing methods pool statistical significance scores such as p-values or posterior probabilities of peptide-spectrum matches (PSMs) from multiple search engines after high scoring peptides have been assigned to spectra, but these methods lack reliable control of identification error rates as data are integrated from different search engines. We developed a statistically coherent method for integrative analysis, termed MSblender. MSblender converts raw search scores from search engines into a probability score for all possible PSMs and properly accounts for the correlation between search scores. The method reliably estimates false discovery rates and identifies more PSMs than any single search engine at the same false discovery rate. Increased identifications increment spectral counts for all detected proteins and allow quantification of proteins that would not have been quantified by individual search engines. We also demonstrate that enhanced quantification contributes to improve sensitivity in differential expression analyses.
doi:10.1021/pr2002116
PMCID: PMC3128686  PMID: 21488652
integrative analysis; database search; peptide identification

Results 1-5 (5)